Thoughtful Machine Learning
Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machinelearning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. ■■
Apply TDD to write and run tests before you start coding
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Learn the best uses and tradeoffs of eight machine-learning algorithms
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Use real-world examples to test each algorithm through engaging, hands-on exercises
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Understand the similarities between TDD and the scientific method for validating solutions
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Be aware of the risks of machine learning, such as underfitting and overfitting data
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Explore techniques for improving your machine-learning models or data extraction
is a very fascinating “ This read, and it is a great resource for developers interested in the science behind machine learning.
”
—Brad Ediger
author, Advanced Rails
is an awesome “ This book.”
—Starr Horne
cofounder, Honeybadger
pumped about “ Pretty [Matthew Kirk]’s Thoughtful Machine Learning book.
”
—James Edward Gray II
consultant, Gray Soft
Thoughtful Machine Learning
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.
Matthew Kirk is the founder of Modulus 7, a data science and Ruby development consulting firm. Matthew speaks at conferences around the world about using machine learning and data science with Ruby.
US $39.99
Twitter: @oreillymedia facebook.com/oreilly
Kirk
PROGR AMMING/MACHINE LEARNING
Thoughtful Machine Learning A TEST-DRIVEN APPROACH
CAN $41.99
ISBN: 978-1-449-37406-8
Matthew Kirk